class: center, middle, inverse, title-slide # Assessing methods for generating route networks from origin-destionation data ## Jittering, routing, and visualisation GISRUK 2022, Liverpool ### Robin Lovelace, University of Leeds
Data Science Fellow at N. 10
+Rosa Félix, Dustin Carlino, Roger Beecham ### 2022-4-06 --- # Background  --- # Tools of the trade <img src="https://docs.ropensci.org/stplanr/reference/figures/stplanr.png" width="30%" /><img src="https://raw.githubusercontent.com/ropensci/stats19/master/man/figures/logo.png" width="30%" /><img src="https://github.com/Robinlovelace/geocompr/blob/main/images/geocompr_hex.png?raw=true" width="30%" /> -- - Evidence-based policies in government: [Data Science Fellowship at N. 10](https://www.ukri.org/opportunity/esrc-adr-uk-no-10-data-science-fellowships-2021/) ??? - Turing Fellowship - LIDA internship on open transport infrastructure data - Links with DfT, MHCLG, TfNH, international partners -- - Future areas of development: Reproducible Bayesian modelling of proportions (Dirichlet regression), Machine Learning, Decarbonisation Agenda --- --- .left-column[ ### Expertise: Geocomputation Computationally efficient data science tools Intuitive, future proof, scalable code Geographic vector data analysis  Geographic raster data  ] -- .right-column[ <!-- --> Source: Morgan and Lovelace (2020) https://doi.org/10.1177/2399808320942779 ] ??? I am an all-round data scientist with experience with Python, Julia, and command line tools such as Docker and shell scripting for scalable data science applications. I have particular expertise in R and geocomputation with R in particular. --- ### Experience working with OD data Adding value and detail to existing OD data. Source: Lovelace, Félix and Carlino ([2022](https://osf.io/qux6g/) preprint)  --- ### From OD data to travel behaviour Source: Lovelace, Félix and Carlino ([2022](https://osf.io/qux6g/) preprint)  --- ### Validating synthetic OD datasets Source: Lovelace, Félix and Carlino ([2022](https://osf.io/qux6g/) preprint)  --- ### Scalability vs resolution Source: [UKRI CREDS project repo](https://github.com/creds2/od-data)  --- ### OA-WPZ data There are 17,848,366 OA to WPZ records, 170k OAs, 54k WPZ For 5km buffer around London, 1.5 million OD pairs with destinations [](https://rpubs.com/RobinLovelace/863109) --- ### Reproducible example .left-column[ ```r u = "https://github.com/ITSLeeds/od/releases/download/v0.3.1/od_intra_top_sf.geojson" desire_lines_oa_wpz_1k = sf::read_sf(u) oas_in_buffer = sf::read_sf("https://github.com/ITSLeeds/od/releases/download/v0.3.1/oas_in_buffer.geojson") wpz_in_buffer = sf::read_sf("https://github.com/ITSLeeds/od/releases/download/v0.3.1/wpz_in_buffer.geojson") library(tmap) tmap_mode("view") ``` ``` ## tmap mode set to interactive viewing ``` ```r m = tm_shape(desire_lines_oa_wpz_1k) + tm_lines() + tm_shape(oas_in_buffer) + tm_dots(col = "darkgreen") + tm_shape(wpz_in_buffer) + tm_dots(col = "darkred") ``` ] .right-column[